Method and system for continuous, dynamic, adaptive searching based on a continuously evolving personal region of interest

ABSTRACT

Embodiments of the present invention are directed to flexible, user-adapted, continuous searching, on behalf of a particular user, for points of interest relevant to the user&#39;s current location within a specifically computed personal region of interest. In a general case, the personal region of interest is computed as a function of the user&#39;s level of disposition towards the searched-for points of interest. The level of disposition towards the searched-for points of interest may, in turn, be based on two or more of the user&#39;s location, the current date and time, a history of the user&#39;s interaction with the POI-searching system, including user-initiated searches and user selections from displayed search results, a user profile developed for, and continuously updated on behalf of, the user, and a current context for the search, as specified by a search query or by other context-specifying means. The personal region of interest generally defines an abstract area, volume, or hypervolume within which method and system embodiments of the present invention search for points of interest.

TECHNICAL FIELD

The present invention is related to personalized search methods andsystems and, in particular, a method and system for providingpersonalized searches based on continuously evolving regions ofinterest.

BACKGROUND OF THE INVENTION

Search techniques are the cornerstone of information retrieval in manydifferent information-distribution environments and problem domains. Forexample, search methods based on card catalogs and the Dewey DecimalSystem provided the foundation for library research for many decadesprior to the advent of cheap personal computers. For a significantperiod of time, dial-up information systems were the primary searchingtool available for scientific and medical researchers. Currently,Internet search engines are among the most frequently used andhighest-revenue-generating tools provided on the Internet, andelectronic searching is being incorporated into an ever-increasingnumber of different electronic devices, from automobile navigationsystems to cell phones.

One increasingly widely available information-retrieval method offeredto users of various electronic devices involves locating points ofinterest (“POIs”) with respect to a user's current location, asspecified by the user or as detected by global positioning services(“GPS”) devices incorporated into the electronic devices, includingautomobile navigation systems, cell phones, personal digital assistants,mobile personal computers, and other electronic devices. FIGS. 1A-Cillustrate and example of currently available location-based POIsearching. In this example, as shown in FIG. 1A, the user's currentlocation is indicated by a graphical object 102 superimposed on a streetmap. Next, as shown in FIG. 1B, a radius 104 with an endpoint coincidentwith the user's current location specifies a disk-like search area 106within which the user wishes to locate points of interest. The radius104 may be specified by the user or may be a default distance valueincorporated into the location-based POI search system. As shown in FIG.1C, the location-based POI search system then finds and displays thelocations of a class of points of interest specified by the user. Forexample, in FIG. 1C, the user has specified, through a text query orthrough interaction with a user interface, a desire to find gift-cardshops proximal to the user's current location. The location-based POIsearch system has located and displayed the locations of three gift-cardshops 108-110. Although the display formats, query inputs, and otherfeatures and characteristics of various location-based POI searchsystems vary, currently available location-based search systemsessentially display a map for a region that includes a user's locationand that is annotated with points of interest corresponding to a searchquery or, alternatively, may provide a list of POIs and correspondingaddresses and/or travel directions.

FIG. 2 illustrates the data components of the location-based POI searchsystem described above, with reference to FIGS. 1A-C. The location-basedPOI search system employs a map database 202 as well as apoints-of-interest database 204. These two database components, alongwith software-encoded logic for receiving, parsing, and executing searchrequests and displaying search results, enable location-based POI searchqueries to be fielded from, and location-based POI search results to bereturned to, users of a large number of different types of electronicdevices, from personal computers to automotive navigation systems andcell phones.

FIG. 3 is a simple control-flow diagram that illustrates logical stepsexecuted by a generalized, currently-available location-based POI searchsystem, such as the location-based POI search system discussed above,with reference to FIGS. 1A-C. In step 302, a location is received from auser, either specified by the user through a user interface, or obtainedvia a GPS component or other location-determining component of anelectronic device. Next, in step 304, a suitable map corresponding tothe received location is searched for, and retrieved from, the mapdatabase, and indication of the user's location is superimposed on themap, as shown in FIG. 1A. Next, in step 306, a search query is receivedfrom the user. The search query may be typed into a user interface,selected from an icon display on a touch screen, or generated by other,similar means. Then, in step 308, the radius of the search area isobtained or determined. The radius of the search area may be specifiedby a user, or retrieved as a default value from memory or from adatabase. Then, in step 310, the POI database is accessed in order toidentify relevant POIs and their corresponding locations within thesearch area, according to the specification of desired POIs representedby the received search query. Finally, in step 312, indications of theidentified POIs within the search area are superimposed graphically ontothe map and displayed to the user. Many of these steps may be combinedtogether in various types of search systems, and, as discussed above,lists of POIs may be returned, rather than annotated maps.

Location-based POI searches provide great convenience and utility tousers of various devices. However, the currently available POI searchsystems and methods, discussed above with reference to FIGS. 1-3, areassociated with a number of deficiencies. First, currently available POIsearching generally returns the same results to any user specifying aparticular type of search. For example, a location-based search forsporting-goods stores would return the same result to a sophisticatedprofessional athlete or professional team manager who has traveled to acity for an upcoming professional sporting event as returned ahigh-school student looking for a pair of sneakers close to home. Theprofessional athlete or professional team manager would likely not beinterested in general department stores with shoe departments that sellsneakers, while the high-school student likely would not be interestedin a high-end store selling professional sports equipment. Moreover, theprofessional athlete or professional team manager may be willing totravel many miles for a necessary piece of equipment, while thehigh-school student might be willing to walk or ride a bus to adestination within a distance of no more than several miles from home.POI searches are not well-tailored to particular users, particularuser's contexts, and particular user's circumstances, and generallyreturn a large amount of unneeded and unappreciated information, whileoften not returning results that would be of great benefit to aparticular user. For this reason, users of location-based POI searchsystems, designers and vendors of location-based POI-search software,and designers, manufacturers, and vendors of a wide variety ofelectronic devices that feature location-based POI searching have allrecognized the need for location-based POI searching better adapted toparticular users.

SUMMARY OF THE INVENTION

Embodiments of the present invention are directed to flexible,user-adapted, continuous searching, on behalf of a particular user, forpoints of interest relevant to the user's current or projected locationwithin a specifically computed personal region of interest. In a generalcase, the personal region of interest is computed as a function of theuser's level of disposition towards the searched-for points of interest.The level of disposition towards the searched-for points of interestmay, in turn, be based on two or more of the user's current or projectedlocation, the current date and time, a history of the user's locationand interaction with the POI-searching system, including user-initiatedsearches and user selections from displayed search results, a userprofile developed for, and continuously updated on behalf of, the user,and a current context for the search, as specified by a search query orby other context-specifying means. The personal region of interestgenerally defines an abstract area, volume, or hypervolume within whichmethod and system embodiments of the present invention search for pointsof interest.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-C illustrate and example of currently available location-basedPOI searching.

FIG. 2 illustrates the data components of the location-based searchengine described above, with reference to FIGS. 1A-C.

FIG. 3 is a simple control-flow diagram that illustrates logical stepsin execution of a location-based POI search, such as the location-basedPOI search discussed above, with reference to FIGS. 1A-C.

FIG. 4 illustrates database components of certain general method andsystem embodiments of the present invention.

FIG. 5 is a control-flow diagram of the interests-tracking component ofgeneral method and system embodiments of the present invention withrespect to a particular user.

FIG. 6 is a control-flow diagram for the routine “select POI,” called instep 510 of FIG. 5, which represents an embodiment of the presentinvention.

FIG. 7 graphically illustrates location-based POI searching according tovarious system and method embodiments of the present invention.

FIG. 8 shows a simple, disk-like-area representation of an PRI.

FIG. 9 illustrates the concept of a “level of disposition.”

FIGS. 10A-D illustrate the types of PAI, a special case of PRI, involvedin three example points-of-interest searches.

FIGS. 11A-11I illustrate an exemplary contacts search.

FIGS. 12A-G illustrate an exemplary food-related POI search.

FIGS. 13A-G illustrate an exemplary collectibles-related POI search.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is directed to highly user-adapted, continuous,dynamic, and adaptive location-based POI searching methods and systems.Unlike currently available location-based POI searching methods andsystems, method and system embodiments of the present invention maintainuser profiles and user histories that constantly evolve through userinteraction with the location-based POI-searching systems of the presentinvention. Location-based POI searching, according to method and systemembodiments of the present invention, is based on a personal region ofinterest that is computed, on a search-by-search basis, from the valuesof various parameters and various types of stored data in differentmethod and system embodiments of the present invention.

In contrast to the two-database-component location-based POI searchingdescribed with reference to FIGS. 1-3, the general method and systemembodiments of the present invention employ a greater number of datacomponents and more sophisticated, software-implemented logic. FIG. 4illustrates database components of certain general method and systemembodiments of the present invention. The database components include aPOI database 402 and a map database 404, as employed by currentlyavailable methods. However, general method and system embodiments of thepresent invention also employ a user-profiles database 406, auser-histories database 408, and a database of various time-associatedevents 410. The database components shown in FIG. 4 are not exhaustive.Additional database components may be employed in various embodiments ofthe present invention, and fewer database components may be employed inother embodiments of the present invention. Moreover, the databasecomponents are not necessarily separate databases, or discretelyimplemented, but may be implemented, for example, as different sets oftables in a single, extensive relational database, or may be implementedas two or more databases of various types residing locally or remotely.In addition, the types of stored information and databases, discussedabove, may instead be obtained by secondary searching of variousinformation sources, including the world-wide web, electronicinformation streams, and various types of broadcast information.

In contrast to currently available methods, method and systemembodiments of the present invention track users' interactions withsearch-system embodiments over time in order to track and continuouslyrefine a stored representation of the users' interests with respect to avariety of different contexts. FIG. 5 is a control-flow diagram of theinterests-tracking component of general method and system embodiments ofthe present invention with respect to a particular user. Thecontrol-flow diagram of FIG. 5 is represented as an endless loop, withuser-interest tracking carried out continuously or at regular intervalsas a user moves through, and interacts with, the user's environment andwith the search system. In step 502, the interests-tracking method waitsfor a next event. When a next event occurs, the interest-tracking methoddetermines what type of event has occurred, and responds accordingly. Ifa location-update event is detected, in step 504, then the user'scurrent or projected location is updated, in step 506. The user'slocation may be stored in the user's history, or in some other databaseor data-storage location, including an electronic memory. If, instead,the detected event corresponds to a user inquiry or user search, asdetermined in step 508, then the routine “select POI” is called, in step510. The routine “select POI” is discussed below, in greater detail. Ifthe detected event corresponds to selection, by the user, of a displayedPOI for additional information or other interaction, as determined instep 512, then the user's history is correspondingly updated, in step514, so that, over time, the user's history can be employed to refinePOI searches as well as to update and refine a user's profile, or, inother words, stored user's preferences. If the detected event is a userprofile update, as determined in step 516, then the user's profile isupdated, in step 518. A user may explicitly, through interaction withthe system, specify or update preferences stored in the user's profile.Alternatively, a user-profile-update event may be generated as a resultof other activities and events, including updates to a user's history orselection by a user of displayed results.

If the detected event corresponds to detection, by the system, of newtime-associated events, as determined in step 520, then thetime-associated-events database is updated in step 522. Just as thesearch system continuously or periodically tracks a user's location andmonitors a user's interaction with the system, the search system alsotracks various information sources in order to accumulate timelyinformation about various events and occurrences that may be relevant toPOI searching. As one example, POI searching for musical events may relyon continuous detection and updating of a calendar of musical eventsstored in the time-associated-events database. If the detected event isa new point of interest, as determined in step 524, then the POIdatabase is updated in step 526. Just as the system continuouslymonitors information sources in order to detect a variety of differenttime-associated events, the system also monitors information sources inorder to identify new points of interest for inclusion in the POIdatabase. Any of various other events, detected in step 528, areaccordingly handled in step 530. The interests-tracking method shown inFIG. 5 is carried out by system embodiments of the present invention onbehalf of all users. Interests tracking, in turn, provides POI searchesinitiated by users, or automatically initiated due to detection ofassociated events, such as changes in users' current or projectedlocations, or various interactions of the user with the system, that areas specifically tailored to individual users as possible, so that thePOI searches return current, up-to-date POI information, as constrainedby the user's current preferences, accumulated history, and theup-to-date time-associated events stored in the time-associated-eventsdatabase.

FIG. 6 is a control-flow diagram for the routine “select POI,” called instep 510 of FIG. 5, which represents an embodiment of the presentinvention. In step 602, the routine “select POI” receives an inquiryfrom a user and determines a context for a POI search. The user mayexplicitly enter search criteria, through a user interface, may indicatesearch criteria through interaction with an icon-based, touch screen, orother such interactive system, or the search inquiry may be generated byother events, such as changes in the user's current or projectedlocation, access by the user to various features of an electronicdevice, such as a cell phone, and may be triggered by other events andactivities. A context is a collection of search-subject constraints andsearch-subject-related parameters associated with a search. A contextmay be largely derived from a search query, but may also be derived fromadditional information, including information stored for a user by thelocation-based POI search system.

In step 604, a personal region of interest (“PRI”) is computed based onthe user's level of disposition (“LoD”), in turn based on two or moreof: (1) the user's current location; (2) the current time and date; (3)the user's location and POI selection history; (4) the user'spreferences stored in the user's profile; (5) the context determinedabove in step 602; (6) a projected user location; (7) a future time anddate; and (8) any of numerous other types of information relevant tocomputing the LoD with respect to the context for the search. Specificexamples of LoDs and PRIs are discussed, below. Then, in step 606, thePOI database is searched to select POIs within the computed PRI thatmeet various constraints embodied in the context, user's history, user'spreferences, and as correlated with time-associated events. In step 608,the map database is accessed to retrieve a relevant map at a relevantscale. Then, in step 610, the retrieved POIs are mapped to the relevantmap retrieved in step 608. Finally, the relevant map with indications ofthe selected POIs is displayed to the user in step 612. It is importantto note that the PRI computed in step 604 is an abstract area, volume,or hypervolume, rather than a fixed, disk-like geographical area ascomputed by currently available methods, discussed above with referenceto FIG. 1B. Because the PRI is abstract, the PRI may not represent asimple, geometrical area or volume, such as the disk-shaped search areashown in FIG. 1B. In alternative embodiments of the present invention,results may be returned as lists of POIs along with addresses and/ortravel directions, rather than as map annotations, or lists of POIsalong with times and other relevant information for non-geographic POIs.

FIG. 7 graphically illustrates POI searching according to various systemand method embodiments of the present invention. The POI searchingbegins with a user's location 702, either current or projected. Theuser's location is continuously monitored by the system as, for example,by continuously monitoring GPS or other location-determining-componentsignals produced by electronic equipment carried by the user.Alternatively, the user's location may be monitored by user interactionwith various personal electronic devices, including personal computers,or by the user's interaction with various other devices, such as ATMmachines, airport ticket machines, credit-card-processing machines,telephones, and other such devices. In other cases, the startinglocation may be a projected location, where the user will travel to at afuture point in time. Next, a PRI 704 is computed from the user's LoD,the LoD computed from, in general, the user's location l, the currenttime and date t, the user's location and POI selection history H, theuser's preferences P, and the search context C. The PRI is a fundamentalconstraint on POI searching. In certain instances, the LoD may becomputed on fewer than the above-discussed number of input parameters,and in other embodiments of the present invention, additional inputparameters may be used. The computed PRI may then be populated by POIsobtained in an initial search based on the computed PRI, the currentcontext C, and the current time t 706. These initially selected POIs maythen be filtered, as well as supplemented, based on the user'spreferences 708 and on the user's history 710. In general, filteringdecreases the number of POIs, although additional POIs may be added as aresult of consideration of the user's preferences and histories.Finally, the POIs are mapped to geographical locations, in the case ofgeographically located POIs, or mapped to some other specified space712. As an example of additional types of spaces, POIs may be searchedfor with respect to proximity in time, rather than, or in additional to,geographical location, and the selected POIs may be displayed along atimeline relative to the current time, rather than as geographicalpositions relative to the current user's location. In other cases, bothlocation and time may be considered, and in still additional cases,various other parameters may be considered, including cost, productspecification, associated features, and many other such considerationsand dimensions.

At an abstract level, a PRI may be conceptually imagined to be adisk-shaped region specified by a radius. FIG. 8 shows a simple,disk-like-area representation of a PRI. However, as noted above, a PRImay be a computed volume, rather than an area, in cases wherethree-dimensional locations are relevant, or in cases where additional,non-geographic dimensions are employed. The PRI may be ahyperdimensional volume in a higher-dimensional space. Moreover, theradius 802 by which a PRI 804 is specified may represent a singleconstraint, such as distance, or multiple constraints, such as time anddistance, distance and convenience, or many other such dimensions and/orconsiderations. As one example, a user currently located in a particulardepartment, on a particular floor, of a multi-story department store orshopping mall may wish to search for points of interest within athree-dimensional volume proximal to the user's location, rather than atwo-dimensional area on the floor on which the user is currentlylocated. As another example, a user may wish to search for POIs within acertain distance of the user's current position as well as POIsreachable within some maximum amount of time, regardless of distance.These criteria may become quite complex. For example, the user may haveseparate time, distance, and convenience thresholds for different typesof available transportation that, in turn, depend on the user'slocation. For example, the user may be willing to travel four miles orup to 15 minutes by subway, but only one-half mile and up to 20 minutesby foot.

In non-geographical spaces, a wide variety of different types ofconstraints may be employed to define a LoD from which a PRI iscomputed. For example, in a search for books on the Internet, the LoDmay be defined by timeliness of shipping, publication date, and othersuch considerations.

FIG. 9 illustrates the concept of a “level of disposition.” In FIG. 9,each disk-like object, such as disk-like object 901, represents aparticular LoD value, or, more particularly, a PRI computed from aparticular LoD values. In general, LoDs are constrained to fall betweena maximum LoD, represented by the maximally sized PRI 902, and a minimumLoD, represented by the minimally sized PRI 904. The maximum and minimumLoD can be computed based on the user's history, preferences, and thecurrent context. A computed LOD, based on additional factors that mayinclude current location, current time, projected location, projectedtime, various time-associated events, and other such factors, can thenbe thought of as falling within the range of possible LoD values withinthe range of LoD values between the maximally sized LoD 902 andminimally sized LoD 904, shown at intervals along an LoD axis 906. Inother words, PRIs are generally bounded by maxima and minima, withranges of possible values, and with particular values selected by avarious parameters and characteristics that together specify a level ofdisposition.

Next, three different simple examples of PRI-and-location-based POIsearching according to method and system embodiments of the presentinvention are provided, with reference to FIGS. 10A-13G. FIGS. 10A-Dillustrate the types of PRI involved in these examples. In all threeexamples, the PRIs are personal areas of interest (“PAIs”), a special,two-dimensional case of the more general PRI. In general. PRIs aredirectly computed from LoDs, so LoD and PRI or PAI are usedinterchangeably. In general, as shown in FIG. 10A, the PAI 1002 iscentered at a particular waypoint, or current or projected userlocation, W, and has an area determined by the distance of the waypointW from the user's home, the time T recently spent by the user at thewaypoint W, and the search criteria of the search for which the PAI iscomputed. In one example, as shown in FIG. 10B, the PAI is computed tofacilitate a search for food-related points of interest, and the area ofthe PAI ranges from a small area 1004 at locations distant from theuser's home to a large area 1006, for a food-related search when theuser is at home. The general concept is that a user is more willing totravel further, at home, than in distant locations in order to find asuitable restaurant. However, of course, this general concept may bemodified, over time, by feedback 1008 collected as a user history, byexplicitly specified or implicitly derived user preferences 1010, or bythe passage of time should the user remain in a distant, non-homelocation In another example, as shown in FIG. 10C, a search is made forpersonal contacts, with the distance a user is willing to travel to meeta personal acquaintance or friend greater at a location distant from theuser's home than at the user's home, reflected in the larger PAI 1014for contact searches at distant locations than the smaller PAI 1014 forcontact searches at home. In yet another example, as shown in FIG. 10D,the PAIs 1016 associated with distance locations and home locations haveidentical areas, suggesting that a user's willingness to findcollectibles is generally location independent. Again, in the latter twoexamples, the PAI computation may also be directed by additionalinformation, including history and preference information.

In a first example, introduced above with reference to FIG. 10C, twoacquainted users Mark and Joe may search for personal contacts,including each other, both at their respective homes in Seattle and at adistant location, in the example Manhattan. FIGS. 11A-11I illustratethis exemplary contacts search. As shown in FIG. 11A, the PAIs at Mark'sand Joe's homes 1102 and 1104 are smaller, in area, than the PAIscomputed for Mark and Joe (1106 and 1108, respectively) at distantlocations. In other words, the two acquaintances, who live within twomiles of each other and who might get together occasionally when athome, are much more eager to meet when both happen to be in distantlocations, even when, at the distant locations, they are separated by aconsiderably larger distance than the distance of separation of theirhomes.

A user-profile database contains tables or views that represent Mark'sand Joe's level of relationship, as shown in FIGS. 11B-C. The level ofrelationship may be user-defined, adapted, and/or inferred from adegrees-of-separation analysis, for example. A degrees-of-separationanalysis may be based on one or more user communities in which Mark andJoe participate, and subgroups within such communities to which Mark andJoe belong. A weight can be associated with each level of relationshipfactor, with respect to a contact search, and can also be user-defined,adapted, and/or inferred from a degrees-of-separation analysis of usercommunities in which Mark and Joe participate. FIG. 11D shows a table orview that represents both Mark's and Joe's weights associated withrelationship factors. FIG. 11E shows a table or view that representsMark's and Joe's base dispositions to meet an acquaintance or friend atvarious distances from home. These tables can be used to computeadjusted dispositions for Mark and Joe to meet people associated withdifferent relationship factors, as represented by the tables or viewsshown in FIGS. 11F and 11G. The table or view shown in FIG. 11Hindicates the radius for a computed PAI associated with different,possible level-of-disposition values. The PAI radii for a contact searchinitiated by Mark and Joe when both are in Manhattan are easily obtainedfrom the above-described tables. In this example, Joe's disposition tomeet any contact considered a “colleague” is 6, which corresponds to aPAI with a 32-mile radius. Mark's disposition to meet any contactconsidered as a “friend” is 7, which corresponds to a PAI with a 44-mileradius. In this scenario, a search for contacts by Joe in Manhattanwould not return Mark, but a search for contacts by Mark in Manhattanwould return Joe. Finally, the table or view shown in FIG. 11I indicatesthat, as Joe and Mark spend more time at the distant locations, theirdispositions to meet contacts may decrease. For example, after 180 days,Mark's overall disposition to meet any contact identified as a “friend”is 3, which corresponds to a PAI with a 10-mile radius.

The second example, introduced with reference to FIG. 10B, concerns asearch for food-related points of interest. FIGS. 12A-G illustrate thisexemplary food-related POI search. As shown in FIG. 12A, PAIs 1202,1204, and 1206 reflect searches for increasingly desirable types offood-serving establishments, and are all considerably smaller than thePAI for food-related POI searches at a user's home location 1208. FIG.12B shows a table or view indicating categories of interest related tofood-serving establishments. FIG. 12C shows a table or view representingdisposition weights associated with each category of interest. FIG. 12Dshows base dispositions with respect to selected distances from home.FIG. 12D shows the adjusted dispositions for the user Joe when inManhattan for each category of interest, obtained by applying the basedisposition of “1” for locations greater than 1500 miles from home, inthe table or view shown in FIG. 12D, to the disposition weights for eachcategory, in the table or view shown in FIG. 12C. The table or viewshown in FIG. 12F shows PAI radii for walking or driving for each of 11possible level of dispositions, and FIG. 12G shows the increase, overtime, of disposition with time spent at a distant location. In thisexample, a search for food-serving establishments by Joe, directly afterarriving in Manhattan, returns: (1) a fast-food chain right next door tothe hotel that matches Joe's disposition of 0, corresponding to a PAIwith a 50-foot radius; (2) a local specialty Italian restaurant thatmatches Joe's disposition of 3, corresponding to a PAI with an 800-footradius; and (3) a Todai Japanese restaurant that matches Joe'sdisposition of 5, corresponding to a PAI with a 9-mile radius. The firsttwo PAIs are based on walking to the restaurants, while the third isbased on driving. However, after 3 days, Joe's overall disposition toseek his favorite restaurant Todai is 10, which corresponds to a PAIwith a 3800-foot radius, when walking, and a PAI with a 26-mile radius,when driving.

The third example, introduced with reference to FIG. 10D, concerns asearch for collectibles. FIGS. 13A-G illustrate this exemplarycollectibles-related POI search. In this example, as shown in FIG. 13A,the PAIs computed for various categories of collectibles at Mark'sSeattle home location 1302, 1304, and 1306 are equal, in size, to PAIscomputed for the same categories of collectibles at the distant locationof Manhattan 1308, 1310, and 1312. The tables or views shown in FIGS.13B and 13C include various categories of interest for collectibles andthe disposition weights for the categories, respectively. The basedisposition for collectibles is contained in the table or view shown inFIG. 13D, which, when applied to the disposition weights for variouscategories of interest, provide the adjusted dispositions for thecategories of interest contained in the table or view shown in FIG. 13E.Radii of PAIs for various disposition levels are shown in FIG. 13F, andthe static nature of the PAIs with respect to time spent at a locationis reflected in the table or view shown in FIG. 13G. In this example, asearch by Mark returns information regarding a rabbit's foot thatmatches Mark's disposition of 5, corresponding to a PAI with a 20-mileradius, information regarding a Ming-dynasty vase that matches Mark'sdisposition of 7, corresponding to a PAI with a 34-mile radius, and aMona Lisa painting that matches Mark's disposition of 10, correspondingto a PAI with a radius of 105 miles.

Thus, the location-based POI searching methods and systems of thepresent invention consider stored information particular to a user, aswell as a search context, to compute, for each search conducted onbehalf of a user, a PRI within which POIs are selected for display tothe user. In the above discussion, PRIs are circular, spherical, orhyperspherical, but, in alternative embodiments, may have othergeometries and configurations. For example, PRIs may be square orrectangular, when PRIs are defined by city-block distance, rather thanby straight line distance. PRIs are not necessarily reflective ofgeographical areas, timeline distances, or metrics associated with otherreal-world spaces, since PRIs may be defined by many different types ofconstraints. For example, a PRI constrained by a maximum distance of 4miles or by a maximum time of travel of 20 minutes may have outerboundaries that are quite non-circular, instead resembling an octopuswith tentacles corresponding to major arterials or subway lines. Asdiscussed above, a user's current location may be geographical, but alsomay be a location in time, or in other types of dimensioned spaces.

Although the present invention has been described in terms of particularembodiments, it is not intended that the invention be limited to theseembodiments. Modifications within the spirit of the invention will beapparent to those skilled in the art. For example, as discussed above, alarge variety of different types of PRIs may be computed for a largevariety of different types of location-based POI searches.Location-based POI searching systems that represent embodiments of thepresent invention may employ many different types of databases andstored data, may be implemented in hardware, software, firmware, or acombination of two or more hardware, software, and firmware, usingdifferent programming and circuit-design languages, modularorganization, control structures, variables, and difference in othersuch design parameters. User profiles may include preferences, contacts,favorites, and wish lists. POIs may include retail establishments,friends, transient events, local entertainment and attractions, andmyriad products and services. PRIs may be continuous and connected, ormay be a collection of discreet, smaller continuous regions.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the invention.However, it will be apparent to one skilled in the art that the specificdetails are not required in order to practice the invention. Theforegoing descriptions of specific embodiments of the present inventionare presented for purpose of illustration and description. They are notintended to be exhaustive or to limit the invention to the precise formsdisclosed. Many modifications and variations are possible in view of theabove teachings. The embodiments are shown and described in order tobest explain the principles of the invention and its practicalapplications, to thereby enable others skilled in the art to bestutilize the invention and various embodiments with various modificationsas are suited to the particular use contemplated. It is intended thatthe scope of the invention be defined by the following claims and theirequivalents:

1. A location-based points-of-interest searching system comprising:stored information; and searching logic that receives apoints-of-interest search request from a user, computes a personalregion of interest with respect to the user and the receivedpoints-of-interest search request, searches for points of interestwithin the personal region of interest, and returns, to the user, one ormore points-of-interest found by searching for points of interest withinthe personal region of interest.
 2. The location-basedpoints-of-interest search system of claim 1 wherein the searching logic:computes a level of disposition with respect to the received searchrequest and the user; and computes the personal region of interest basedon the level of disposition.
 3. The location-based points-of-interestsearch system of claim 2 wherein the searching logic computes a level ofdisposition with respect to the received search request and the userbased on two or more of: a current location of the user; a futurelocation of the user; a current time; a projected, future time, acurrent date; a projected, future date; the received points-of-interestsearch request; and a portion of the stored information.
 4. Thelocation-based points-of-interest search system of claim 3 wherein thestored information includes one or more of: a database of maps; personalpreferences for the user; location and POI selection history for theuser; a database of points of interest; and time-associated events. 5.The location-based points-of-interest search system of claim 3 whereinthe current and projected location of a user may be a geographicallocation, a spatial location, a location in time, or a location in adimensioned space, the search system further including: monitoring logicthat continuously or periodically update stored information related tothe users' preferences and histories.
 6. The location-basedpoints-of-interest search system of claim 1 wherein the personal regionof interest is computed from one or more of: a current location of theuser; a context at least in part obtained from the receivedpoints-of-interest search request; user preferences stored in a userprofile; the user's history of interaction with the user's environmentand with the location-based points-of-interest search system;time-associated events stored by the location-based points-of-interestsearch system; and the current date and time; and a portion of thestored information, and searches for points of interest within thepersonal region of interest.
 7. The location-based points-of-interestsearch system of claim 1 wherein the personal region of interest is oneof: a geographical region; a spatial, three-dimensional region; aportion of a timeline; and a volume or hypervolume in ahyper-dimensional space. a spatial location, a location in time; and alocation in any dimensioned space.
 8. The location-basedpoints-of-interest search system of claim 1 further including:monitoring logic that monitors the user's interactions with thelocation-based points-of-interest search system and with the user'senvironment to continuously or periodically update stored informationrelated to the user's preferences and histories.
 9. The location-basedpoints-of-interest search system of claim 1 wherein the personal regionof interest computed for a search for food-serving establishments has afirst size when the user's location is within a threshold distance fromthe user's home, and wherein the personal region of interest computedfor a search for food-serving establishments has a second size smallerthan the first size when the user's location is beyond the thresholddistance from the user's home.
 10. The location-based points-of-interestsearch system of claim 1 wherein the personal region of interestcomputed for a search for personal contacts has a first size when theuser's location-based points-of-interest searching system returnspoints-of-interest search results to users as one or more of: is withina threshold distance from the user's home, and wherein the personalregion of interest computed for the search for personal contacts has asecond size larger than the first size when the user's location isbeyond the threshold distance from the user's home.
 11. Thelocation-based points-of-interest search system of claim 1 wherein thesearching logic returns, to the user, one or more points-of-interestfound by searching for points of interest within the personal region ofinterest by one or more of: returning a map that includes the personalregion of interest and annotations; returning a graphically displayedlist of points of interest, accompanied with location specifying text;returning an audio rendering of a list of for the one or morepoints-of-interest found by searching for points of interest,accompanied with location specifying text; and; returning graphicallydisplayed icons.
 12. A method for searching for points of interest onbehalf of a user, the method comprising. receiving a points-of-interestsearch request from the user, computing a personal region of interest,with respect to the user and the received points-of-interest searchrequest; selecting points of interest within the personal region ofinterest, and returning, to the user, the selected points of interest.13. The method of claim 12 wherein the personal region of interest isone of: a geographical region; a spatial, three-dimensional region; aportion of a timeline; and a volume or hypervolume in ahyper-dimensional space.
 14. The method of claim 12 wherein a length,area, or volume of the region of interest reflects a level ofdisposition of the user for the types of points of interest specified bythe received points-of-interest search request, modified by variousadditional parameters and values including: distance of the user fromthe user's home; and time spent by the user at the current or projectedlocation of the user.
 15. The method of claim 12 wherein the level ofdisposition is computed with respect to the received search request andthe user based on two or more of: a current location of the user; afuture location of the user; a current time; a projected, future time; acurrent date; a projected, future date; the received points-of-interestsearch request; a portion of the stored information; a contextspecified, at least in part, by the received points-of-interest searchrequest; and additional stored information.
 16. The method of claim 15wherein the additional stored information includes one or more of: adatabase of maps; personal preferences for the user; histories oflocation and POI selection interactions for the user; a database ofpoints of interest; and time-associated events.
 17. The method of claim12 further including: continuously or periodically monitoring the user'scurrent location.
 18. The method of claim 17 wherein the user's currentlocation is one of: a geographical location; a spatial location; alocation in time; and a location in any dimensioned space.
 19. Themethod of claim 12 further including: monitoring the user's interactionswith the location-based points-of-interest search system and with theuser's environment to continuously or periodically update storedinformation related to the user's preferences and histories.
 20. Themethod of claim 12 wherein the personal region of interest computed fora search for food-serving establishments has a first size when theuser's location is within a threshold distance from the user's home, andwherein the personal region of interest computed for a search forfood-serving establishments has a second size smaller than the firstsize when the user's location is beyond the threshold distance from theuser's home.
 21. The method of claim 12 wherein the personal region ofinterest computed for a search for personal contacts has a first sizewhen the user's location is within a threshold distance from the user'shome, and wherein the personal region of interest computed for thesearch for personal contacts has a second size larger than the firstsize when the user's location is beyond the threshold distance from theuser's home.
 22. The method of claim 12 wherein returning, to the userthe selected points-of-interest further includes: returning a map thatincludes the personal region of interest and annotations for the one ormore points-of-interest found by searching for points of interest;returning a list of one or more points-of-interest found by searchingfor points of interest; and displaying one or more points-of-interestfound by searching for points of interest on a graphical user interface.